def parse_args(description=__doc__):
"""Parse command-line arguments"""
parser = ArgumentParser(description=description)
parser.add_argument('--outdir', required=True)
group = parser.add_argument_group(title='Scan parameters')
for dim in HYPO_PARAMS_T._fields:
group.add_argument(
'--{}'.format(dim.replace('_', '-')),
nargs='+',
required=True,
help='''Hypothses will take this(these) value(s) for dimension
{dim_hr}. Specify a single value to not scan over this dimension;
specify a human-readable string of values, e.g. '0, 0.5, 1-10:0.2'
scans 0, 0.5, and from 1 to 10 (inclusive of both endpoints) with
stepsize of 0.2.'''.format(dim_hr=dim.replace('_', ' ')))
split_kwargs = init_obj.parse_args(dom_tables=True,
hypo=True,
events=True,
parser=parser)
split_kwargs['scan_kw'] = split_kwargs.pop('other_kw')
if split_kwargs['events_kw']['hits'] is None:
raise ValueError('Must specify a path to a pulse series or photon'
' series using --hits.')
return split_kwargs
def main():
"""Script to run Retro recos in icetray"""
parser = ArgumentParser()
parser.add_argument(
"--input-i3-file",
type=str,
required=True,
nargs="+",
help="""Input I3 file""",
)
parser.add_argument(
"--output-i3-file",
type=str,
required=True,
help="""Output I3 file""",
)
split_kwargs = init_obj.parse_args(dom_tables=True,
tdi_tables=True,
parser=parser)
other_kw = split_kwargs.pop("other_kw")
# instantiate Retro reco object
my_reco = Reco(**split_kwargs)
tray = I3Tray()
tray.AddModule(
_type="I3Reader",
_name="reader",
FilenameList=other_kw["input_i3_file"],
)
tray.Add(
_type=my_reco,
_name="retro",
methods="crs_prefit",
reco_pulse_series_name="SRTTWOfflinePulsesDC",
hit_charge_quant=0.05,
min_hit_charge=0.25,
seeding_recos=["L5_SPEFit11", "LineFit_DC"],
triggers=["I3TriggerHierarchy"],
additional_keys=["L5_oscNext_bool"],
filter='event["header"]["L5_oscNext_bool"] and len(event["hits"]) >= 8',
point_estimator="median",
)
tray.AddModule(
_type="I3Writer",
_name="writer",
DropOrphanStreams=[icetray.I3Frame.DAQ],
filename=other_kw["output_i3_file"],
)
tray.AddModule(_type="TrashCan", _name="GoHomeYouReDrunk")
tray.Execute()
tray.Finish()
def main(description=__doc__):
"""Script main function"""
parser = ArgumentParser(description=description)
parser.add_argument('--outdir', required=True)
split_kwargs = init_obj.parse_args(dom_tables=True, parser=parser)
generate_stacked_tables(dom_tables_kw=split_kwargs['dom_tables_kw'],
**split_kwargs['other_kw'])
def parse_args(description=__doc__):
"""Parse command-line arguments"""
parser = ArgumentParser(description=description)
parser.add_argument(
'--outdir', required=True,
)
parser.add_argument(
'--sim-to-test', required=True, choices=sorted(SIMULATIONS.keys())
)
return init_obj.parse_args(parser=parser, dom_tables=True, hypo=True)
def parse_args(description=__doc__):
"""Parse command-line arguments"""
parser = ArgumentParser(description=description)
parser.add_argument(
'--outdir',
required=True,
)
parser.add_argument(
'--sim-to-test',
required=True,
)
dom_tables_kw, hypo_kw, _, pdf_kw = (init_obj.parse_args(parser=parser,
hits=False))
return dom_tables_kw, hypo_kw, pdf_kw
def parse_args(description=__doc__):
"""Parse command-line arguments.
Returns
-------
split_kwargs : dict of dicts
Contains keys "dom_tables_kw", "hypo_kw", "events_kw", and "reco_kw",
where values are kwargs dicts usable to instantiate or call each of the
corresponding objects or functions.
"""
parser = ArgumentParser(description=description)
parser.add_argument('--outdir', required=True)
group = parser.add_argument_group(title='Hypothesis parameter priors', )
group.add_argument(
'--spatial-prior',
choices='dc dc_subdust ic SPEFit2'.split(),
required=True,
help='''Choose a prior for choosing spatial samples. "dc", "dc_subdust"
and "ic" are uniform priors with hard cut-offs at the extents of the
respective volumes, while "SPEFit2" samples from Cauchy distributions
around the SPEFit2 (x, y, z) best-fit values.''')
group.add_argument(
'--temporal-prior',
choices='uniform SPEFit2'.split(),
required=True,
help='''Choose a prior for choosing temporal samples. "uniform" chooses
uniformly from 4000 ns prior to the first hit up to the last hit, while
"SPEFit2" samples from a Cauchy distribution near (not *at* due to
bias) the SPEFit2 time best-fit value.''')
group.add_argument(
'--energy-prior',
choices=[PRI_UNIFORM, PRI_LOG_UNIFORM, PRI_LOG_NORMAL],
required=True,
help='''Prior to put on _total_ event energy. Must specify
--energy-lims.''')
group.add_argument(
'--energy-lims',
nargs='+',
required=True,
help='''Lower and upper energy limits, in GeV. E.g.: --energy-lims=1,100
Required if --energy-prior is {}, {}, or {}'''.format(
PRI_UNIFORM, PRI_LOG_UNIFORM, PRI_LOG_NORMAL))
group = parser.add_argument_group(title='MultiNest parameters', )
group.add_argument(
'--importance-sampling',
action='store_true',
help='''Importance nested sampling (INS) mode. Could be more efficient,
but also can be unstable. Does not work with multimodal.''')
group.add_argument(
'--max-modes',
type=int,
required=True,
help=
'''Set to 1 to disable multi-modal search. Must be 1 if --importance-sampling is
specified.''')
group.add_argument('--const-eff',
action='store_true',
help='''Constant efficiency mode.''')
group.add_argument('--n-live', type=int, required=True)
group.add_argument('--evidence-tol', type=float, required=True)
group.add_argument('--sampling-eff', type=float, required=True)
group.add_argument(
'--max-iter',
type=int,
required=True,
help='''Note that iterations of the MultiNest algorithm are _not_ the
number of likelihood evaluations. An iteration comes when one live
point is discarded by finding a sample with higher likelihood than at
least one other live point. Such a point can take many likelihood
evaluatsions to find.''')
group.add_argument(
'--seed',
type=int,
required=True,
help='''Integer seed for MultiNest's random number generator.''')
split_kwargs = init_obj.parse_args(dom_tables=True,
hypo=True,
events=True,
parser=parser)
split_kwargs['reco_kw'] = reco_kw = split_kwargs.pop('other_kw')
if reco_kw['energy_prior'] in [
PRI_UNIFORM, PRI_LOG_UNIFORM, PRI_LOG_NORMAL
]:
assert reco_kw['energy_lims'] is not None
elims = ''.join(reco_kw['energy_lims'])
elims = [float(l) for l in elims.split(',')]
reco_kw['energy_lims'] = elims
elif reco_kw['energy_lims'] is not None:
raise ValueError(
'--energy-limits not used with energy prior {}'.format(
reco_kw['energy_prior']))
return split_kwargs